def save_descriptors_keypoints(SLIDES_PATH, VID_FRAMES_PATH, SLIDE_START,
SLIDE_END, VID_START, VID_END,
SLIDES_KP_SAVE_PATH, VID_KP_SAVE_PATH):
## getting filenames
videoFrameNames = [
filename for filename in os.listdir(VID_FRAMES_PATH)
if filename.endswith(".png")
]
videoFrameNames = slidesInRange(videoFrameNames, VID_START, VID_END)
slideNames = [
filename for filename in os.listdir(SLIDES_PATH)
if filename.endswith(".png")
]
slideNames = slidesInRange(slideNames, SLIDE_START, SLIDE_END)
## calculating, saving keypoints
slideNameKPDesc = dict()
for slideName in slideNames:
path = SLIDES_PATH + slideName
img = cv2.imread(SLIDES_PATH + slideName, 0)
keypoints, descriptors = calculateKeypoints(img)
serialized = serializableKeypts(keypoints, descriptors)
print("Saving " + path)
slideNameKPDesc[path] = serialized
pickleOutput = open(SLIDES_KP_SAVE_PATH + "slideKeyptsDesc.pkl", 'wb')
pickle.dump(slideNameKPDesc, pickleOutput)
vidNameKPDesc = dict()
for vidName in videoFrameNames:
path = VID_FRAMES_PATH + vidName
img = cv2.imread(VID_FRAMES_PATH + vidName, 0)
keypoints, descriptors = calculateKeypoints(img)
print("Saving " + path)
serialized = serializableKeypts(keypoints, descriptors)
vidNameKPDesc[path] = serialized
pickleOutput = open(VID_KP_SAVE_PATH + "vidKeyptsDesc.pkl", 'wb')
pickle.dump(vidNameKPDesc, pickleOutput)
## where to load/save
slideImgPath = "../slides/"
vidFramePath = "../bb_sampled_video_frames/"
slideImgKeyptsFile = "./slide_keypts/slideKeyptsDesc.pkl"
vidImgKeyptsFle = "./vid_keypts/vidKeyptsDesc.pkl"
matchesPath = "./desc_matches/"
## getting filenames
videoFrameNames = [
filename for filename in os.listdir(vidFramePath)
if filename.endswith(".jpg")
]
videoFrameNames = slidesInRange(videoFrameNames, 0, 28)
slideNames = [
filename for filename in os.listdir(slideImgPath)
if filename.endswith(".png")
]
slideNames = slidesInRange(slideNames, 2, 30)
## load dictionary of things
slideImgKeypts = pickle.load(open(slideImgKeyptsFile, "rb"))
vidImgKeypts = pickle.load(open(vidImgKeyptsFle, "rb"))
slideImgKeyptsDesc = dict()
vidImgKeyptsDesc = dict()
## load slide img, keypoints
vidImgKeyptsFle = "./new_vid_keypts/vidKeyptsDesc.pkl"
savePath = "./new_cross_matches_save/"
VID_START = 0
VID_END = 22
SLIDE_START = 0
SLIDE_END = 12
NUM_SLIDES = VID_END - VID_START - 1
## getting filenames
videoFrameNames = [
filename for filename in os.listdir(vidFramePath)
if filename.endswith(".png")
]
videoFrameNames = slidesInRange(videoFrameNames, VID_START, VID_END)
slideNames = [
filename for filename in os.listdir(slideImgPath)
if filename.endswith(".png")
]
slideNames = slidesInRange(slideNames, SLIDE_START, SLIDE_END)
sampleImgSlides = cv2.imread(slideImgPath + slideNames[0], 0)
sampleVidSlides = cv2.imread(vidFramePath + videoFrameNames[0], 0)
## slide and video dimensions
slideDimensions = (sampleImgSlides.shape[0], sampleImgSlides.shape[1])
vidDimensions = (sampleVidSlides.shape[0], sampleVidSlides.shape[1])
## load dictionary of things
def compute_match_save(SLIDES_PATH, SLIDE_START, SLIDE_END,
FRAME_SAMPLE_SAVE_FOLDER,
SLIDE_KEYPTS_DESCS_PKL_SAVE_PATH, VID_START, VID_END,
VID_KEYPTS_DESCS_PKL_SAVE_PATH,
FINAL_FIGURES_RESULTS_SAVE_PATH):
NUM_SLIDES = SLIDE_END - SLIDE_START
NUM_FRAMES = VID_END - VID_START
## getting filenames
videoFrameNames = [
filename for filename in os.listdir(FRAME_SAMPLE_SAVE_FOLDER)
if filename.endswith(".png")
]
videoFrameNames = slidesInRange(videoFrameNames, VID_START, VID_END)
slideNames = [
filename for filename in os.listdir(SLIDES_PATH)
if filename.endswith(".png")
]
slideNames = slidesInRange(slideNames, SLIDE_START, SLIDE_END)
sampleImgSlides = cv2.imread(slideImgPath + slideNames[0], 0)
sampleVidSlides = cv2.imread(vidFramePath + videoFrameNames[0], 0)
## slide and video dimensions
slideDimensions = (sampleImgSlides.shape[0], sampleImgSlides.shape[1])
vidDimensions = (sampleVidSlides.shape[0], sampleVidSlides.shape[1])
## load dictionary of things
slideImgKeypts = pickle.load(open(SLIDE_KEYPTS_DESCS_PKL_SAVE_PATH, "rb"))
vidImgKeypts = pickle.load(open(VID_KEYPTS_DESCS_PKL_SAVE_PATH, "rb"))
slideImgKeyptsDesc = dict()
vidImgKeyptsDesc = dict()
## load slide img, keypoints
for sn in slideNames:
path = slideImgPath + sn
slideImgKeyptsDesc[sn] = convertPickledToKPDesc(slideImgKeypts[path])
for vidName in videoFrameNames:
path = vidFramePath + vidName
vidImgKeyptsDesc[vidName] = convertPickledToKPDesc(vidImgKeypts[path])
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
## matches is a list of DMatch objects
flann = cv2.FlannBasedMatcher(index_params, search_params)
matchSlideFrameDict = dict()
matchMetric = dict()
matchMetricNotNormalized = dict()
## cross matching
matchID = 0
for slide in range(0, NUM_SLIDES):
for frame in range(0, NUM_FRAMES):
print("Slide, Frame", (slide, frame), slideNames[slide],
videoFrameNames[frame])
slidekpts, slideDescs = slideImgKeyptsDesc[slideNames[slide]]
framekpts, frameDescs = vidImgKeyptsDesc[videoFrameNames[frame]]
matches = flann.knnMatch(np.asarray(slideDescs, np.float32),
np.asarray(frameDescs, np.float32),
k=2)
matchPairs = []
distances = []
good = []
slideKeypoints = []
frameKeypoints = []
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
slideKeypoints.append(slidekpts[m.queryIdx].pt)
frameKeypoints.append(framekpts[m.trainIdx].pt)
good.append(m)
'''
slideImg = cv2.imread(slideImgPath + slideNames[slide], 0)
vidImg = cv2.imread(vidFramePath + videoFrameNames[frame], 0)
saveMatches(slidekpts, framekpts, slideImg, vidImg, good, savePath + str(slide) + '-' + str(frame) + '-match.jpg')
'''
matchMetric[(slide, frame)] = computeSumDiffKeypoints(
slideKeypoints, frameKeypoints, slideDimensions, vidDimensions)
matchMetricNotNormalized[(slide, frame)] = computeSumDiffKeypoints(
slideKeypoints, frameKeypoints, slideDimensions, vidDimensions,
False)
## save visualizations
mat = visualize(
matchMetric, NUM_SLIDES, NUM_FRAMES, FINAL_FIGURES_RESULTS_SAVE_PATH +
"normalized_sumDistanceKeypoints.jpg")
visualize(matchMetricNotNormalized, NUM_SLIDES, NUM_FRAMES,
FINAL_FIGURES_RESULTS_SAVE_PATH + "sumDistanceKeypoints.jpg")
## save matrix as txt
np.savetxt(FINAL_FIGURES_RESULTS_SAVE_PATH +
"slides_by_vidframes_metric.txt",
mat,
fmt='%d')
col_best_prediction = {}
for col in range(NUM_FRAMES):
slides = list(mat[:, col])
best = slides.index(min(slides))
col_best_prediction[col] = best
print(json.dumps(col_best_prediction, sort_keys=True))
import cv2
import os
import pickle
from utilities import visualize, drawMatches, predictARGMIN
from utilities import calculateKeypoints, serializableKeypts, slidesInRange, convertPickledToKPDesc, computeRDistanceDifference, computeSumDiffKeypoints
import numpy as np
from matplotlib import pyplot as plt
## plot number of matches
## where to load/save
slideImgPath = "./360-video-10-23/"
## Load slides, compute keypoint matching to following slide
slideNames = [ filename for filename in os.listdir( slideImgPath) if filename.endswith(".jpg")]
slideNames = slidesInRange(slideNames, 0, 74)
## load dictionary of things
slideImgKeyptsFile = "./slide_keypts/slideKeyptsDesc.pkl"
slideImgKeypts = pickle.load( open( slideImgKeyptsFile, "rb" ) )
print(slideImgKeypts.keys())
slideImgKeyptsDesc = dict()
## load slide img, keypoints
for sn in slideNames:
path = slideImgPath + sn
slideImgKeyptsDesc[sn] = convertPickledToKPDesc(slideImgKeypts[path])
import numpy as np
from matplotlib import pyplot as plt
SLIDE_PATH = "./360-video-10-23/"
TARGET_SLIDES = [54, 58, 61, 67, 71, 72]
VIDEO_PATH = "./subsampled_slides_video/cropped_shortened_pres.mp4"
VIDEO_START_SEC = 0
VIDEO_END_SEC = 63
VIDEO_FRAME_PATH = "./vid_frames_sampled/"
SAVE_MATCH_PATH = "./frames_slides_matches/"
slideNames = [
filename for filename in os.listdir(SLIDE_PATH)
if filename.endswith(".jpg")
]
slideNames = slidesInRange(slideNames, 0, 74)
videoFrameNames = [
filename for filename in os.listdir(VIDEO_FRAME_PATH)
if filename.endswith(".jpg")
]
videoFrameNames = slidesInRange(videoFrameNames, 0, 74)
## slide and video dimensions
sampleImgSlides = cv2.imread(SLIDE_PATH + slideNames[0], 0)
sampleVidSlides = cv2.imread(VIDEO_FRAME_PATH + videoFrameNames[0], 0)
slideDimensions = (sampleImgSlides.shape[0], sampleImgSlides.shape[1])
vidDimensions = (sampleVidSlides.shape[0], sampleVidSlides.shape[1])
slideImgKeyptsFile = "./slide_keypts/slideKeyptsDesc.pkl"
vidImgKeyptsFle = "./vid_keypts/vidKeyptsDesc.pkl"
slideImgKeypts = pickle.load(open(slideImgKeyptsFile, "rb"))
